{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex     bmi  children smoker     region      charges\n",
       "0   19  female  27.900         0    yes  southwest  16884.92400\n",
       "1   18    male  33.770         1     no  southeast   1725.55230\n",
       "2   28    male  33.000         3     no  southeast   4449.46200\n",
       "3   33    male  22.705         0     no  northwest  21984.47061\n",
       "4   32    male  28.880         0     no  northwest   3866.85520"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "data = pd.read_csv('./data/insurance.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([536., 398., 129.,  86.,  35.,  59.,  57.,  32.,   2.,   4.]),\n",
       " array([ 1121.8739  ,  7386.729311, 13651.584722, 19916.440133,\n",
       "        26181.295544, 32446.150955, 38711.006366, 44975.861777,\n",
       "        51240.717188, 57505.572599, 63770.42801 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8ddb94b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "plt.hist(data['charges'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 46., 105., 101., 155., 198., 283., 156., 123., 115.,  56.]),\n",
       " array([ 7.02275569,  7.42678461,  7.83081352,  8.23484244,  8.63887136,\n",
       "         9.04290027,  9.44692919,  9.8509581 , 10.25498702, 10.65901594,\n",
       "        11.06304485]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8cd2baac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.log(data['charges']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f8e30c6588>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8e3046cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.kdeplot(data.loc[data.sex=='male', 'charges'], shade=True, label='male')\n",
    "sns.kdeplot(data.loc[data.sex=='female', 'charges'], shade=True, label='female')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f8e30a41d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8e30999b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data.loc[data.region=='northwest', 'charges'], shade=True, label='northwest')\n",
    "sns.kdeplot(data.loc[data.region=='southwest', 'charges'], shade=True, label='southwest')\n",
    "sns.kdeplot(data.loc[data.region=='northeast', 'charges'], shade=True, label='northeast')\n",
    "sns.kdeplot(data.loc[data.region=='southeast', 'charges'], shade=True, label='southeast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f8e30a8668>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8e315cac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data.loc[data.smoker=='yes', 'charges'], shade=True, label='smoker yes')\n",
    "sns.kdeplot(data.loc[data.smoker=='no', 'charges'], shade=True, label='smoker no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f8e332be80>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8e332b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data.loc[data.children==0, 'charges'], shade=True, label='children 0')\n",
    "sns.kdeplot(data.loc[data.children==1, 'charges'], shade=True, label='children 1')\n",
    "sns.kdeplot(data.loc[data.children==2, 'charges'], shade=True, label='children 2')\n",
    "sns.kdeplot(data.loc[data.children==3, 'charges'], shade=True, label='children 3')\n",
    "sns.kdeplot(data.loc[data.children==4, 'charges'], shade=True, label='children 4')\n",
    "sns.kdeplot(data.loc[data.children==5, 'charges'], shade=True, label='children 5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     bmi  children smoker      charges\n",
       "0   19  27.900         0    yes  16884.92400\n",
       "1   18  33.770         1     no   1725.55230\n",
       "2   28  33.000         3     no   4449.46200\n",
       "3   33  22.705         0     no  21984.47061\n",
       "4   32  28.880         0     no   3866.85520"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['region', 'sex'], axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>under</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>over</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>over</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>under</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>under</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age    bmi children smoker      charges\n",
       "0   19  under       no    yes  16884.92400\n",
       "1   18   over      yes     no   1725.55230\n",
       "2   28   over      yes     no   4449.46200\n",
       "3   33  under       no     no  21984.47061\n",
       "4   32  under       no     no   3866.85520"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def greater(df, bmi, num_child):\n",
    "    df['bmi'] = 'over' if df['bmi'] >= bmi else 'under'\n",
    "    df['children'] = 'no' if df['children'] == num_child else 'yes'\n",
    "    return df\n",
    "\n",
    "data = data.apply(greater, axis=1, args=(30, 0))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>charges</th>\n",
       "      <th>bmi_over</th>\n",
       "      <th>bmi_under</th>\n",
       "      <th>children_no</th>\n",
       "      <th>children_yes</th>\n",
       "      <th>smoker_no</th>\n",
       "      <th>smoker_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>16884.92400</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1725.55230</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>4449.46200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>21984.47061</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>3866.85520</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age      charges  bmi_over  bmi_under  children_no  children_yes  \\\n",
       "0   19  16884.92400         0          1            1             0   \n",
       "1   18   1725.55230         1          0            0             1   \n",
       "2   28   4449.46200         1          0            0             1   \n",
       "3   33  21984.47061         0          1            1             0   \n",
       "4   32   3866.85520         0          1            1             0   \n",
       "\n",
       "   smoker_no  smoker_yes  \n",
       "0          0           1  \n",
       "1          1           0  \n",
       "2          1           0  \n",
       "3          1           0  \n",
       "4          1           0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.get_dummies(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi_over</th>\n",
       "      <th>bmi_under</th>\n",
       "      <th>children_no</th>\n",
       "      <th>children_yes</th>\n",
       "      <th>smoker_no</th>\n",
       "      <th>smoker_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  bmi_over  bmi_under  children_no  children_yes  smoker_no  smoker_yes\n",
       "0   19         0          1            1             0          0           1\n",
       "1   18         1          0            0             1          1           0\n",
       "2   28         1          0            0             1          1           0\n",
       "3   33         0          1            1             0          1           0\n",
       "4   32         0          1            1             0          1           0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data.drop('charges', axis=1)\n",
    "y = data['charges']\n",
    "x.fillna(0, inplace=True)\n",
    "y.fillna(0, inplace=True)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_features = PolynomialFeatures(degree=2, include_bias=False)\n",
    "x_train_poly = poly_features.fit_transform(x_train)\n",
    "x_test_poly = poly_features.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(x_train_poly, np.log1p(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge = Ridge()\n",
    "ridge.fit(x_train_poly, np.log1p(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=100, n_iter_no_change=None, presort='auto',\n",
       "             random_state=None, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "booster = GradientBoostingRegressor()\n",
    "booster.fit(x_train_poly, np.log1p(y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y_predict = reg.predict(x_test_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_ridge = ridge.predict(x_test_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_boost = booster.predict(x_test_poly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.38612871902910473,\n",
       " 0.36726512388917415,\n",
       " 4526.346164017067,\n",
       " 4870.945538831188)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "log_rmse_train = np.sqrt(mean_squared_error(y_true=np.log1p(y_train), y_pred=reg.predict(x_train_poly)))\n",
    "log_rmse_test = np.sqrt(mean_squared_error(y_true=np.log1p(y_test), y_pred=y_predict))\n",
    "rmse_train = np.sqrt(mean_squared_error(y_true=y_train, y_pred=np.exp(reg.predict(x_train_poly))))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_true=y_test, y_pred=np.exp(reg.predict(x_test_poly))))\n",
    "\n",
    "log_rmse_train, log_rmse_test, rmse_train, rmse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3861483471673159, 0.3674145329917445, 4528.301421018692, 4874.6144518888195)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_rmse_train = np.sqrt(mean_squared_error(y_true=np.log1p(y_train), y_pred=ridge.predict(x_train_poly)))\n",
    "log_rmse_test = np.sqrt(mean_squared_error(y_true=np.log1p(y_test), y_pred=y_predict_ridge))\n",
    "rmse_train = np.sqrt(mean_squared_error(y_true=y_train, y_pred=np.exp(ridge.predict(x_train_poly))))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_true=y_test, y_pred=np.exp(ridge.predict(x_test_poly))))\n",
    "\n",
    "log_rmse_train, log_rmse_test, rmse_train, rmse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3600122862648176,\n",
       " 0.36850333631417226,\n",
       " 4194.3561232608345,\n",
       " 4747.7079905705605)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_rmse_train = np.sqrt(mean_squared_error(y_true=np.log1p(y_train), y_pred=booster.predict(x_train_poly)))\n",
    "log_rmse_test = np.sqrt(mean_squared_error(y_true=np.log1p(y_test), y_pred=y_predict_boost))\n",
    "rmse_train = np.sqrt(mean_squared_error(y_true=y_train, y_pred=np.exp(booster.predict(x_train_poly))))\n",
    "rmse_test = np.sqrt(mean_squared_error(y_true=y_test, y_pred=np.exp(booster.predict(x_test_poly))))\n",
    "\n",
    "log_rmse_train, log_rmse_test, rmse_train, rmse_test"
   ]
  }
 ],
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